As the deployment of fifth generation (5G) mobile wireless networks continues to gain momentum, researchers are already focusing on the challenges and opportunities of the next sixth generation (6G). To meet the ever-increasing demand for higher data rates and support the development of new services, 6G is expected to exploit millimeter wave (mmWave) frequencies. However, the complex propagation characteristics at mmWave require beamforming technology, which introduces significant complexity in the communication system. Herein, we propose a real-time testbed platform to evaluate beamforming and other communication solutions designed for multiple-input multiple-output (MIMO) mmWave-based 6G networks. This platform serves as an enabler for 6G technologies evaluation under realistic propagation conditions, accelerating the development and deployment of robust and efficient 6G networks. To demonstrate the capabilities of our platform, we have implemented a smart relay with real-time beam control and tracking. The platform is able to perform an exhaustive search of 64 reception beams in less than 256 μs. Additionally, the platform can maintain the optimal beam even in mobility scenarios using a gradient-based tracking system that achieves a low overhead of less than 5%, with an update rate of 100 Hz.

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle’s ego-motion, requiring very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this setting, radar data are typically used to refine the navigation-based trajectory estimation with so-calledautofocusalgorithms. Although widely used in remote sensing applications, where the timeliness of the imaging is not an issue, autofocus in automotive scenarios calls for simple yet effective processing options to enable real-time environment imaging. This paper aims at providing a comprehensive theoretical and experimental analysis of the autofocusrequirementsin typical automotive scenarios. We analytically derive the effects of navigation-induced trajectory estimation errors on SAR imaging, in terms of defocusing and wrong targets’ localization. Then, we propose a motion estimation and compensation workflow tailored to automotive applications, leveraging a set of stationary Ground Control Points (GCPs) in the low-resolution radar images (before SAR focusing). We theoretically discuss the impact of the GCPs position and focusing height on SAR imaging, highlighting common pitfalls and possible countermeasures. Finally, we show the effectiveness of the proposed technique employing experimental data gathered during open road campaign by a 77 GHz multiple-input multiple-output radar mounted in a forward-looking configuration.

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (UML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less).

Automated driving requires a huge number of on-board sensors to provide advanced functionalities, from parking assistance to emergency braking and environment mapping for target recognition/classification. While low-cost automotive-legacy radars are mostly used for target detection due to their limited angular resolution, vehicular Synthetic Aperture Radar (SAR) is emerging as a promising imaging solution, provided that the motion is known with high accuracy. This paper assesses the benefits of a navigation-augmented SAR system exploiting multiple on-board sensors, e.g., Global Navigation Satellite System (GNSS), Inertial Measurement Units (IMUs), odometers and steering angle sensors. The results confirm the potential of the proposed multi-sensor-aided SAR system to obtain centimeter-level accurate images of the driving scenario.

Complex phenomena are generally modeled with sophisticated simulators that, depending on their accuracy, can be very demanding in terms of computational resources and simulation time. Their time-consuming nature, together with a typically vast parameter space to be explored, make simulation- based optimization often infeasible. In this work, we present a method that enables the optimization of complex systems through Machine Learning (ML) techniques. We show how well-known learning algorithms are able to reliably emulate a complex simulator with a modest dataset obtained from it. The trained emulator is then able to yield values close to the simulated ones in virtually no time. Therefore, it is possible to perform a global numerical optimization over the vast multi-dimensional parameter space, in a fraction of the time that would be required by a simple brute-force search. As a testbed for the proposed methodology, we used a network simulator for next-generation mmWave cellular systems. After simulating several antenna configurations and collecting the resulting network-level statistics, we feed it into our framework. Results show that, even with few data points, extrapolating a continuous model makes it possible to estimate the global optimum configuration almost instantaneously. The very same tool can then be used to achieve any further optimization goal on the same input parameters in negligible time.

Large antenna arrays and millimeter- wave (mmWave) frequencies have been attracting growing attention as possible candidates to meet the high requirements of future 5G mobile networks. In view of the large path loss attenuation in these bands, beamforming techniques that create a beam in the direction of the user equipment are essential to perform the transmission. For this purpose, in this paper, we aim at characterizing realistic antenna radiation patterns, motivated by the need to properly capture mmWave propagation behaviors and understand the achievable performance in 5G cellular scenarios. In particular, we highlight how the performance changes with the radiation pattern used. Consequently, we conclude that it is crucial to use an accurate and realistic radiation model for proper performance assessment and system dimensioning.

In this paper, we investigate a wireless communication scenario, where polarization multiplexing is exploited to increase the spectral efficiency. Independent modems over each polarization are considered, with communication links affected by phase noise and cross-polarization interference (XPI). We devise a novel per-polarization soft decision-directed iterative receiver with separatea posterioriprobability-based synchronization and decoding. The synchronization algorithm relies on a minimum mean square error-based master–slave phase estimation followed by the cancellation of the XPI on the polarization of interest and requires no statistical knowledge of the phase noise process. The performance of the proposed iterative receiver is investigated for a pilot symbol-assisted low-density parity-check-coded quadrature amplitude modulation scheme.

This study discusses synchronisation in phase noise‐impaired spectrally efficient communication systems employing high‐order modulations. In particular, an iterative receiver, where demodulation and decoding are separate from maximum a posteriori probability (MAP) synchronisation, is presented. The authors’ separate approach is tailored to the design of pragmatic iterative receiver schemes employing ‘off‐the‐shelf’ demodulation and decoding blocks. This allows full compatibility with already existing systems, which is attractive from the implementation viewpoint. The proposed MAP synchronisation algorithm also requires very limited knowledge of the phase noise process and achieves near coherent performance with moderate computational complexity. Although the approach is very general, the authors discuss its performance for low‐density parity‐check‐coded pilot symbol‐aided quadrature amplitude modulation schemes, demonstrating that a significantly lower computational complexity can be achieved with respect to benchmark joint receivers.

This paper focuses on phase noise-impaired communications. An efficient Maximum A-posteriori Probability (MAP) iterative synchronization algorithm, where detection and decoding are performed separately from phase estimation, is proposed. This approach has the following key advantages: (i) its computational complexity is relatively low and its performance is near optimal; (ii) it requires very limited statistical knowledge of the phase noise process; and (iii) it enables the direct use of “off-the-shelf” demodulation and decoding blocks. These features are particularly attractive from the implementation viewpoint, as they lead to the design of effective pragmatic high-order coded modulated schemes. The proposed iterative synchronization and decoding algorithm, evaluated for Low-Density Parity-Check (LDPC)-coded pilot symbol-assisted Quadrature Amplitude Modulation (QAM) schemes, entails a negligible energy efficiency loss with respect to optimized joint decoding and phase estimation approaches, with significantly lower computational complexity.